Development of a high-efficiency rotary dryer for sewage sludge

A rotary drum dryer having an internal rotating body was designed and tested in this study. It was shown that the developed dryer is effective for drying sewage sludge. The best operating conditions in the dryer were low energy input and almost 10% moisture content. The conditions are 255°C for the rotary drum temperature, 17 min for the sludge residence time, and 55 kg/m³ h for the dryer load. Under these conditions, the drying efficiency was 84.8%. The average diameter of dried sludge was less than 8 mm, and the weight reduction rate was 80%. Parametric screening studies achieved the following results. The drying efficiency increased with the increase of the internal temperature and the sludge residence time in the rotary drum, while the drying efficiency decreased when increasing the dryer load. In addition, it was shown that NH₃ and CO₂ were the primary components released from the sewage sludge drying process. The amounts of both of these components increased when the rotary drum temperature was increased.     

Storage stability of injection-molded starch-zein plastics under dry and humid conditions

Corn starch and zein mixtures (4 : 1 dry weight) were extruded and injection-molded in the presence of plasticizers (glycerol and water). Tensile strength and percentage elongation of the molded plastics were measured before and after 1 week of storage under a dry or humid condition (11 or 93% RH). With 10–12% glycerol and 6–8% water, injection-molded plastics had relatively good tensile properties (20- to 25-MPa tensile strength and 3.5–4.7% elongation). But while exposed to dry conditions (11% RH), the molded plastics lost weight (0.5–1.5% in 7 days) and became very brittle, with significant decreases in tensile strength and elongation. Partial replacement (5–10%) of starch with a maltodextrin (average DE 5) reduced the glass transition and melting temperatures of the starch-zein mixture as well as the dry storage stability. Using potato starch instead of corn starch significantly improved the dry storage stability of the injection-molded starch-zein plastics (18- vs 11-MPa tensile strength). Anionic corn starches with a maleate or succinate group (DS<0.01) produced injection-molded plastics with improved tensile properties and storage stability. Plastics prepared from the starch maleate and zein mixture retained the strength during 1 week of dry storage without a significant change (26-MPa tensile strength and 3.7% elongation after 1 week of storage).Paper presented at the Bio/Environmentally Degradable Polymer Society—Second National Meeting, August 19–21, 1993, Chicago, Illinois.Journal paper No. J-15561 of the Iowa Agriculture and Home Economics Experiment Station, Ames, Iowa, Project No. 2863.     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

EFFECTS OF INITIAL ABSTRACTION AND URBANIZATION ON ESTIMATED RUNOFF USING CN TECHNOLOGY1

ABSTRACT: Few studies have been conducted to explore the effects of initial abstraction on estimated direct runoff despite the widespread use of the curve number (CN) method in many hydrologic models to estimate direct runoff. In this study, use of a 5 percent ratio of initial abstraction (I_a) to storage (S) to estimate daily direct runoff with modified CN values for a 5 percent I_a/S value was investigated using the Long‐Term Hydrologic Impact Assessment (L‐THIA) geographic information system (GIS). In addition, the effects on estimated runoff of altering the hydrologic soil group due to urbanization were investigated. The L‐THIA model was applied to the Indiana Little Eagle Creek watershed with 5 percent and 20 percent I_a/S values, considering hydrologic soil group alteration due to urbanization. The results indicate that uses of a 5 percent l_a/S and modified CN values and Hydrologic Soil Group D for urbanized areas in model runs can improve long term direct runoff prediction.     

Treatment of Perchlorate‐Contaminated Groundwater Using Highly Selective,Regenerable Ion‐Exchange Technology: A Pilot‐Scale Demonstration

Treatment of perchlorate‐contaminated groundwater using highly selective, regenerable ion‐exchange technology has been recently demonstrated at Edwards Air Force Base, California. At an influent concentration of about 450 μg/l ClO₄^?, the bifunctional anion‐exchange resin bed treated approximately 40,000 empty bed volumes of groundwater before a significant breakthrough of ClO₄^? occurred. The presence of relatively high concentrations of chloride and sulfate in site groundwater did not appear to affect the ability of the bifunctional resin to remove ClO₄^?. The spent resin bed was successfully regenerated using the FeCl₃^?HCl regeneration technique recently developed at the Oak Ridge National Laboratory, and nearly 100 percent of sorbed ClO₄^? was displaced or recovered after elution with as little as about two bed volumes of the regenerant solution. In addition, a new methodology was developed to completely destroy ClO₄^? in the FeCl₃^?HCl solution so that the disposal of perchlorate‐containing hazardous wastes could be eliminated. It is therefore anticipated that these treatment and regeneration technologies may offer an efficient and cost‐effective means to remove ClO₄^? from contaminated groundwater with significantly reduced generation of waste requiring disposal. © 2002 Wiley Periodicals, Inc.     

A dynamic compartment model for assessing the transfer of radionuclide deposited onto flooded rice-fields

A dynamic compartment model has been studied to estimate the transfer of radionuclides deposited onto flooded rice-fields after an accidental release. In the model, a surface water compartment and a direct shoot-base absorption from the surface water to the rice-plant, which are major features discriminating the present model from the existing model, has been introduced to account for the flooded condition of rice-fields. The model has been applied to the deposition experiments of 137Cs on rice-fields that were performed at three different times to simulate the deposition before transplanting (May 2) and during the growth of the rice (June 1 and August 12), respectively. In the case of the deposition of May 2, the root-uptake is the most predominant process for transferring 137Cs to the rice-body and grain. When the radionuclide is applied just after transplanting (June 1), the activity of the body is controlled by the shoot-base absorption and the activity of the grain by the root-uptake. The deposition just before ear-emergence (August 12) shows that the shoot-base absorption contributes entirely to the increase of both the activities of the body and grain. The model prediction agrees within one or two factors with the experimental results obtained for a respective deposition experiment.     

Odor and gas release from anaerobic treatment lagoons for swine manure

Odor and gas release from anaerobic lagoons for treating swine waste affect air quality in neighboring communities but rates of release are not well documented. A buoyant convective flux chamber (BCFC) was used to determine the effect of lagoon loading rate on measured odor and gas releases from two primary lagoons at a simulated wind speed of 1.0 m s(-1). Concentrations of ammonia (NH3), hydrogen sulfide (H2S), carbon dioxide (CO2), sulfur dioxide (SO2), and nitric oxide (NO) in 50-L air samples were measured. A panel of human subjects, whose sensitivity was verified with a certified reference odorant, evaluated odor concentration, intensity, and hedonic tone. Geometric mean odor concentrations of BCFC inlet and outlet samples and of downwind berm samples were 168 +/- 44 (mean +/- 95% confidence interval), 262 +/- 60, and 114 +/- 38 OU(E) m(-3) (OU(E), European odor unit, equivalent to 123 microg n-butanol), respectively. The overall geometric mean odor release was 2.3 +/- 1.5 OU(E) s(-1) m(-2) (1.5 +/- 0.9 OU s(-1) m(-2)). The live mass specific geometric mean odor release was 13.5 OU(E) s(-1) AU(-1) (animal unit = 500 kg live body mass). Overall mean NH3, H2S, CO2 and SO2 releases were 101 +/- 24, 5.7 +/- 2.0, 852 +/- 307, and 0.5 +/- 0.4 microg s(-1) m(-2), respectively. Nitric oxide was not detected. Odor concentrations were directly proportional to H2S and CO2 concentrations and odor intensity, and inversely proportional to hedonic tone and SO2 concentration (P < 0.05). Releases of NH3, H2S, and CO2 were directly proportional (P < 0.05) to volatile solids loading rate (VSLR).     

Improvement of thermal hydrolysis rate of dichloroacetic acid using alcohol

Dichloroacetic acid (DCAA) is produced during the oxidation of trichloroethylene. It is also produced in drinking water treatment as a disinfection by-product. DCAA is a problem material, because of its toxicity. The objective of this research is to find the final products and the reaction pathway of the DCAA decomposition by hydrolysis, and to increase the hydrolysis rate. The removal of both chlorine atoms in DCAA structure was achieved with hydrolysis at around 75 degrees C, and the final products were oxalic acid and glycolic acid. The reaction pathway was the production of oxalic acid and glycolic acid from two glyoxylic acid molecules by Cannizzaro reaction after the glyoxylic acid production from dechlorination of DCAA with hydrolysis. The hydrolysis rate of DCAA was increased with the use of 90% ethanol solution as solvent. The activation energy of DCAA was about 80 kJ/mol in it, while it was around 105 kJ/mol in water.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.